Ontora

Mid-sized companies and enterprise business units (300-3,000 people) with people-intensive work in services, engineering, support and sales run on manual handoffs and processes nobody has fully mapped. Executives and AI teams tasked with AI transformation strategy struggle to know where to start, since understanding the actual steps, decisions and handoffs behind the work requires interviewing employees across teams and building analysis themselves. The text frames this as costly: every slow handoff and manual task carries a cost in time, money and capacity. Without a structured approach, companies must schedule interviews, collect findings, build their own system architecture connecting documents and tools, assemble implementation teams, and design their own way of measuring impact, all separately.

Ontora interviews employees across teams in parallel to uncover the steps, exceptions and handoffs behind daily work, then turns those conversations into process maps connecting each step to people, tools and evidence. This surfaces where handoffs stall or exceptions add work, producing a prioritised roadmap and a business case for a first AI initiative. Ontora then builds targeted AI solutions around the opportunities the data reveals, agreeing on the business case upfront, implementing with its own engineers, and measuring return against time, cost and capacity. Customers get ongoing access to the Ontora platform, a shared company context layer connected to internal tools via API, MCP and CLI, which brings together company knowledge, processes and evidence for implementation and continued improvement.

The site argues AI transformation is now a business imperative: every slow handoff and manual task carries a cost, and AI transformation turns that daily friction into capacity, margin and room to grow.